Improvement in the Quality of Solutions of a Heuristic Linear Decomposer for Index Generation Functions
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Authors
Nagayama, Shinobu
Sasao, Tsutomu
Butler, Jon T.
Subjects
Linear decomposition
index generation functions
functional decomposition
balanced decision tree
index generation functions
functional decomposition
balanced decision tree
Advisors
Date of Issue
2021-05-25
Date
May 25, 2021
Publisher
IEEE
Language
Abstract
In a multi-valued input classification function, each input combination represents properties of an object, while the output represents the class of the object. Each variable may have different radix. In most cases, the functions are partially defined. To represent multi-valued variables, both one-hot and minimum-length encoding are considered. Experimental results using University of California Irvine (UCI) benchmark functions show that the one-hot approach results in fewer variables than the minimum-length approach with linear decompositions.
Type
Conference Paper
Description
2021 IEEE 51st International Symposium on Multiple-Valued Logic (ISMVL)
Series/Report No
Department
Electrical and Computer Engineering (ECE)
Organization
Naval Postgraduate School (U.S.)
Identifiers
NPS Report Number
Sponsors
This research is partly supported by the JSPS KAKENHI Grant (C), No.19K11881, 2020.
Funder
JSPS KAKENHI Grant (C), No.19K11881
Format
6 p.
Citation
Distribution Statement
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.